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Utilize RWD in regulatory submissions and accelerate research with AI-driven schema conversion
Converting healthcare data into standard formats—such as CDISC SDTM, OMOP, or custom schemas — is a significant bottleneck in life sciences. Manual mapping is slow, expensive, and introduces risks to data fidelity.
Cornerstone accelerates the transformation of diverse source data into high-quality, submission- and analysis-ready formats in a fraction of the time, without sacrificing traceability.
The Challenge
Evaluating quality and cleaning data shouldn’t slow down life sciences research, but it often does:
Mapping Bottleneck
Mapping source data to standard domains, standardizing terminology, and validating compliance can take weeks to months of FTE time.
Fragmented Data Silos
Traditional extract, transform, and load (ETL) pipelines are rigid and often break with slight changes to source data.
Data Integrity Gaps
Errors in source data, such as poor standardization rates, missing values, or anomalies, often carry through to converted outputs.
Auditability
Traceability is critical for regulators, yet preserving a clear, auditable trail from each transformed variable back to source values remains difficult.
Automatically Convert Raw Native Schemas to Analysis-Ready Standards
Mapping
Algorithmic mapping to detect source structure and understand data contents across any native data schema
Standardization
Built-in support for MedDRA, SNOMED, RxNorm, WHODRUG, LOINC, and more
Validation
Automated quality validation and error detection pre/post conversion
Audit Trail
Produce fully harmonized datasets with a comprehensive audit trail
Example Use Cases
CDISC SDTM Conversion
Align and convert RWD and clinical trial schemas to CDISC-SDTM
OMOP Conversion
Convert native real-world data formats to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)
RWD “Stacking” to Single Schema
Combine and harmonize distinct datasets into a single data schema, including some custom formats